This topic develops students’ understanding of how ......

By the end of this unit, students will be able to:

• Understand the European and national laws regulating horticulture, including self-consumption, food safety, and voluntary certifications.

• Learn how the European horticultural market works, including production, harvesting, processing, packaging, and distribution.

• Identify career opportunities for youth in horticulture and develop relevant skills and knowledge for entering the sector.

• Explore entrepreneurship in the green economy, including sustainable business models, innovation, and technology in horticulture.

• Develop an understanding of customer relations and communication, including effective tools, feedback management, and strategies for building customer trust.

• Explain the regulatory framework for horticulture in Europe and their national context.

• Describe the horticultural supply chain, from production to distribution, and identify market trends.

• Recognize skills and career paths for young people in the horticulture sector.

• Analyze sustainable business models and innovative solutions for horticultural enterprises.

• Understand communication strategies to engage customers, manage feedback, and improve customer relations in horticulture.

• Integrate practical knowledge with theoretical concepts through interactive activities and reflection exercises.

EQF levels 3 and/or 4. 

Learning objective: Students design an automatic irrigation system for urban garden on a limited area, taking into account sustainable principles.

Activity procedure:

- Divide students into groups of 2–3.

- Each group receives a sketch of a yard plot with dimensions. The plot of the yard can be planted with various types of crops (vegetables, fruit-bearing shrubs or lawn), which are selected randomly.

- The group must design the installation according to the crops being grown.

- The water irrigation system should include water source, automatic water timer, pressure regulation and valves, filtration, main lines, driplines and fittings (tees, elbows, barbed coupling, end caps, coupling valves, goof plug/emitters/sprinklers) 

- for determine the system parameters can be used the irrigation calculator.

At the end, they present their solution and justify it.

Learning objective: Students set up a smart drip irrigation system for fertilizers mixed with water based on a micro controller board.

Resources required: micro controller board (Arduino UNO/WEMOS D1 ESP 82266/ other), sensors (soil temperature, soil moisture, soil nutrient, pH, humidity, rain), solenoid valve, LCD screen, PC/tablet/mobile and software application

Characteristics of the SMART Irrigation System implementation:

Parameter sensing: The sensors, like soil moisture, temperature and electrical conductivity sensors, are placed in contact with the soil which is to be cultivated. These sensors are interfaced with the controlling unit via wired or wireless connections. 

Coding: The most important step is to write the code for the control unit. For Arduino UNO and Wemos D1 ESP 82266 boards it is recommended to use Arduino IDE Software which is easy and simple for Windows / macOS/ Linux. The Arduino IDE software implementations include simple coding in C/C++ language.

Analysis and Decision Making: Before reading the analog inputs from the sensors, the threshold values for each parameter must be predefined. The sensors are connected to the respective pins of the microcontroller board. 

• Sensors. Sensors are used to measure the physical parameters of the soil and send the readings to the application. If the value is less than the threshold value (which is pre-fed into the board), the solenoid valve will open (dripping system is starting).

• Micro controller board (Arduino UNO/Wemos D1 ESP 82266). We recommend using a micro controller board with open source, like Arduino UNO or Wemos D1 ESP 82266, because for beginners it is the best board to get started with electronics and coding. Arduino UNO is for beginners and does not require an internet connection, instead Wemos D1 ESP 82266 is for more complex project with IoT technology.

Activity procedure:

- The connect the humidity sensor on controller board is made,

- Write the code in Arduino IDE Software which establishes the threshold when the irrigation system opens

- Test the system.

Learning objectives: Develop students' ability to diagnose and manage greenhouse environmental control systems during communication and equipment failures. Students will learn how local SCADA and edge-computing architectures maintain autonomous operation when cloud connectivity is lost, interpret real-time microclimate sensor data to assess plant disease risk and apply systematic troubleshooting procedures to identify whether actuator failures are caused by electrical or mechanical faults.

Technical Problem Description:

You are the on-duty Urban Horticulture Technician managing a high-tech automated greenhouse facility. It is a hot summer morning and a sudden regional storm causes a fiber-optic internet outage across the farm facility, blinding the farm manager's remote cloud dashboard. You walk into the greenhouse control room and access the local Edge Layer computer terminal (the industrial SCADA interface), which is still running independently of the cloud database via local "If-Then" control logic. The aspirated climate box logs display the following microclimate metrics:

Ambient Temperature: 28⁰ C
Relative Humidity (RH): 88%
Leaf Wetness Sensor Status: Active / Wet.
Motorized Roof Vents: Stuck at 0% Open (Closed). 

The local decision support system flashes a bright red alert reading: "CRITICAL WARNING: Fungal Germination Model Active. High Botrytis Cinerea Risk".

Task 1: Network Architecture Evaluation

• Explain why the local greenhouse SCADA terminal can still display real-time sensor metrics and run automation rules even though the external internet connection is completely offline.

• Identify which layer of the three-tier storage architecture is keeping the facility safe during this communications blackout.

Task 2: Prescriptive Actuator Intervention

• The predictive software tries to open the greenhouse roof vents to drive down humidity, but the vents remain shut. Outline your physical troubleshooting steps to verify if this failure is caused by a broken electrical micro-relay signal on the controller board or a mechanical jam in the rack-and-pinion gear motor.

Learning objectives: Develop students' skills in applying precision agriculture technologies for crop monitoring and irrigation management. Students will learn to interpret NDVI maps generated from drone-based remote sensing, analyze sensor data to identify crop stress, diagnose irrigation system failures using field observations and telemetry data, and evaluate the role of variable-rate management in improving water-use efficiency, reducing agricultural inputs, and promoting environmentally sustainable crop production.

Technical Problem Description:

You are a precision horticulture specialist reviewing weekly drone-captured maps of a multi-hectare open-field organic vegetable plot. Your goal is to practice site-specific management to lower input consumption and identify crop stress at an early stage. The drone's software tool processes near-infrared and visible red-light layers to output a color-coded Normalized Difference Vegetation Index (NDVI) map. While 95% of the field shows a healthy canopy score of +0.78 (vibrant green), a localized 20 meters patch in Sector B shows up as bright red with a score of +0.12. You check the farm database logs for Sector B's soil telemetry node. The local Arduino microcontroller board outputs a continuous, unyielding moisture probe value of "1023 units".

Task 1: Spatial Map Interpretation

• Write down the mathematical formula used by your remote sensing tool to process this spatial light data:

NDVI = NIR – Red / NIR + Red

• Explain the scientific reason why healthy plant leaves generate a high positive score on this scale, whereas the stressed leaves in Sector B reflect more visible red light.

Task 2: Mechanical System Repair & VRT Evaluation

• Upon closer inspection of the irrigation lines in Sector B, you find that a long section of thin-walled drip tape has ruptured and burst, preventing water from reaching the plants down the row.

• State the exact maximum pressure restriction (psi) that thin drip tape must stay below to avoid bursting. Identify which failed or missing system component allowed the line pressure to exceed this threshold. 

• Explain how utilizing this digital mapping process protects the environment compared to old-fashioned uniform field care.

Troubleshooting for Scenarios

Possible Solution for Scenario 1: Greenhouse Microclimate Malfunction & Fungal Risk Management:

Task 1: Network Architecture Evaluation

• Why the system runs offline: The greenhouse automation system is engineered with an on-site computing hub that hosts local "If-Then" control logic. This local system aggregates sensor data streams and executes mechanical commands completely independent of an external internet connection. 

• The Architectural Layer in use: The system is relying fully on Tier 2: The Edge Layer. This layer uses local hardware (such as an intelligent on-site IoT gateway or SCADA server) to process real-time rules locally during cloud blackouts.

Task 2: Prescriptive Actuator Intervention

To isolate why the motorized roof vents failed to open, the technician should follow these diagnostic steps: 

• Step 1 (Software Check): Access the microcontroller terminal (such as the Arduino board) to verify if the code is correctly outputting an activation signal when the moisture and temperature thresholds are crossed.

• Step 2 (Electrical Check): Use an electrical multi-tester to measure the continuity and voltage output across the micro-relay board pins to confirm it is successfully delivering power (24V AC or 12V DC) to the vent actuator lines.

• Step 3 (Mechanical Check): If electrical power is reaching the lines but no movement occurs, move to the physical hardware to inspect the rack-and-pinion assembly and steel drive shafts for physical jams, loose gears, or a burned-out electric motor.

Possible Solution for Scenario 2: Remote Sensing Analysis & Smart Drip Irrigation Failure

Task 1: Spatial Map Interpretation

• The Remote Sensing Formula: Students must document the Normalized Difference Vegetation Index equation 

NDVI = NIR – Red / NIR + Red

• The Scientific Reason for Light Variances: Healthy, photosynthetically active crop leaves contain large concentrations of chlorophyll, which strongly absorbs visible red light for energy production but highly reflects near-infrared (NIR) light to avoid overheating. When a crop zone experiences extreme stress, its internal leaf cell walls break down, causing it to reflect far less NIR light and absorb less visible red light, resulting in a low positive NDVI score (+0.12) that flags a clear color anomaly on the map.

Task 2: Mechanical System Repair & VRT Evaluation

• Maximum Tape Pressure Restriction: Thin-walled drip tape is fragile and must be operated strictly below 15 to 20 psi to avoid bursting.

• The Failed System Component: The system failed because it lacked an operational pressure regulator, which is required to prevent water line spikes from exceeding the drip tape's physical limits.

• Environmental Protection via VRT: Traditional uniform field farming applies identical amounts of water and chemicals across a wide region regardless of variation. By utilizing digital mapping and site-specific Variable Rate Technology (VRT), the technician can locate the precise 20 meters failure zone and deliver repairs exclusively to the area of deficiency. This targeted care optimizes overall resource use, maintains regional soil health, prevents the over-application of resources, and reduces the risk of chemical runoff into local groundwater systems.

Learning objective: To learn how to use an online degree-day model to predict the development and occurrence of insect pests and plant diseases based on meteorological data and to identify periods of increased risk for different developmental stages.

Resources required: 

- Each student will work individually.

- Using a PC, laptop, tablet, or mobile device, students will calculate the development of invasive insect pests and plant diseases using the Online Phenology and Degree-Day Models application developed by the National Institute of Food and Agriculture.

-  Prepare the meteorological data in the required text or CSV format.

- Upload the prepared meteorological data file in the STATION section of the application.

- In the MODEL section, select the target species from the Pest and Plant Disease Models database. The application can calculate phenology and bioclimatic indices for 148 insect pests and plant diseases.

- Run the selected model and analyze the results.

- Identify the predicted periods of first occurrence and peak activity for the different developmental stages (e.g., eggs, larvae or nymphs, pupae and adults).

- Interpret the results and discuss their potential application in pest monitoring and integrated pest management (IPM).

- Add in you file the 14-day meteorological forecast data available from Meteoblue.com to run the degree-day model and forecast the risk of pest or plant disease development over the next two weeks.

Learning objective: To learn how to use an AI-powered online tool to identify plant diseases and insect pests and develop appropriate management strategies for maintaining a healthy garden.

Resources required: Tablet or mobile device, internet connection. 

Activity procedure:

- Each student will work individually.

- Using a tablet or mobile device, students will use the Google Lens app to identify different insect species. The identification results will be verified and compared with the Entomology Atlas.

- Using the Google Lens app, students will identify diseases affecting plant leaves and other plant organs (e.g., stems, fruits or flowers).

- Based on the identified pest or disease, students will propose an appropriate action plan, including suitable prevention and control measures.

- Students will present and discuss their findings with the class, comparing the accuracy of the AI tool with traditional identification resources and reflecting on the advantages and limitations of AI in plant health diagnostics.

A